Channel Attention: In SISR, we mainly want to recover as much valuable high-frequency information as possible. However, common CNN-based methods treat channel-wise features equally, which lacks flexibility in dealing with different types of information. To solve this problem, many methods [1]}, [2]} introduce the SE mechanism in the SISR model. For example, Zhang et al. [1]} proposed a new module based on the SE mechanism, named residual channel attention block (RCAB). As shown in Fig. REF , a global average pooling layer followed by a Sigmoid function is used to rescale each feature channel, allowing the network to concentrate on more useful channels and enhancing discriminative learning ability. In SAN [4]}, second-order statistics of features are explored to conduct the attention mechanism based on covariance normalization. A great number of experiments have shown that the second-order channel attention can help the network obtain more discriminative representations, leading to higher reconstruction accuracy.
